| Autor: |
Beauchamp M; Equipe géostatistique, Centre de Géosciences, Mines ParisTech 35, rue Saint Honoré, 77305, Fontainebleau, France. maxime.beauchamp@mines-paristech.fr.; Institut National de l'Environnement Industriel et des Risques (INERIS) Direction des risques chroniques, Parc Technologique Alata, 60550, Verneuil-en-Halatte, France. maxime.beauchamp@mines-paristech.fr., Malherbe L; Institut National de l'Environnement Industriel et des Risques (INERIS) Direction des risques chroniques, Parc Technologique Alata, 60550, Verneuil-en-Halatte, France., de Fouquet C; Equipe géostatistique, Centre de Géosciences, Mines ParisTech 35, rue Saint Honoré, 77305, Fontainebleau, France., Létinois L; Institut National de l'Environnement Industriel et des Risques (INERIS) Direction des risques chroniques, Parc Technologique Alata, 60550, Verneuil-en-Halatte, France. |
| Abstrakt: |
The European legislation on ambient air quality introduces the concepts of spatial representativeness of a monitoring station and spatial extent of an exceedance zone. Spatial representativeness is an essential macro-scale siting criterion which should be evaluated before the setting-up and during the life of a monitoring point. As for the exceedance area, it has to be defined each time an environmental objective is exceeded in an assessment zone. No specific approach is prescribed to delimit such areas. A probabilistic methodology is presented, based on a preliminary kriging estimation of atmospheric concentrations at each point of the domain. It is applied to NO 2 pollution on the urban scale. In the proposed approach, a point belongs to the area of representativeness of a station if its concentration differs from the station measurement by less than a given threshold. To take the estimation uncertainty into account, the standard deviation of the kriging error is used in a probabilistic framework. The choice of the criteria used to deal with overlapping areas is first tested on NO 2 annual mean concentration maps of France, built by combining surface monitoring observations and outputs from the CHIMERE chemistry transport model. At the local scale, data from passive sampling surveys and high -resolution auxiliary variables are used to provide a more precise estimation of the background pollution in different French cities. The traffic-related pollution can also be accounted for in the map by additional predictors such as distance to the road, and traffic-related NO x emissions. Similarly, the proposed approach is implemented to identify the points, at a given statistical risk, where the NO 2 concentration is above the annual limit value. |
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